Exemplary embodiments relate to database systems, and more specifically, to timestamps in database systems.
In modern database systems, high availability and fast query response times are important requirements. Especially in the field of analytical decision support systems, the trend changes from infrequent analyses within large time intervals to mandatory analyses in daily business. Therefore, techniques have been developed by vendors of commercial business intelligence data warehouse systems to provide 24×7 availability and fast query processing for ad hoc reporting. Since updates of warehouse data are mostly processed batch wise in larger time intervals, queries can still be processed in the meantime on the old warehouse state without downtime if a snapshot semantic is used. This leads to an approach used in temporal databases where each tuple has timestamps defining its validity.
To achieve short query response times, massive parallel processing is used where the entire warehouse data is kept in main memory for fast access. Despite the fact that main memory becomes cheaper, main memory is still a critical resource since the amount of data in the warehouses grows dramatically. Thus, it would be good to keep data volumes as low as possible. Furthermore, latency due to main memory access evolves to the new bottleneck in efficient computation because processor speed grows faster than access speed in random access memory (RAM) chips. Therefore, data structures should be designed in a way that the main memory hierarchy is optimally utilized with faster but smaller caches.